Automated microscope platform with improved imaging and accurate neuron reconstruction capabilities for high-throughput studies of neuroregeneration

自动化显微镜平台具有改进的成像和精确的神经元重建能力，适用于神经再生的高通量研究

• 批准号: 10626683
• 负责人: Samuel Hue-Kay Chung
• 金额: $ 49.45万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10626683
• 关键词: Address; Algorithms; Automation; Benchmarking; Caenorhabditis elegans; Cell physiology; Cells; Computers; Data; Data Set; Data Storage and Retrieval; Development; Devices; Equipment and supply inventories; Feedback; Fiber; Gene Expression; Genes; Genetic; Goals; Healthcare; Image; Image Enhancement; Individual; Injury; Invertebrates; Investigation; Knowledge; Laser Surgery; Lesion; Light; Lighting; Location; Machine Learning; Manuals; Maps; Medical; Microscope; Microscopy; Mission; Modeling; Morphology; National Institute of Neurological Disorders and Stroke; Natural regeneration; Nematoda; Nerve Regeneration; Nervous system structure; Neuraxis; Neurites; Neurons; Operative Surgical Procedures; Optic Nerve; Optical Methods; Outcome; Pathway interactions; Peripheral; Process; Public Health; Publications; Quality of life; Regenerative capacity; Regenerative pathway; Regenerative research; Research; Resolution; Role; Sampling; Spinal Cord; Spinal cord injury; System; Techniques; Testing; Three-Dimensional Image; Time; Validation; Visualization; Work; autoencoder; cell growth regulation; central nervous system injury; computerized data processing; conditioning; contrast enhanced; contrast imaging; digital; experimental study; healing; image processing; improved; in vivo; innovation; insight; nervous system disorder; novel; real-time images; reconstruction; regenerative therapy; repaired; therapeutic target; three-dimensional modeling

Project Summary/Abstract The mammalian central nervous system typically fails to regenerate after injury, leading to incurable conditions with immense healthcare burdens. An exception is a remarkable effect called lesion conditioning, where injury to a neuron’s peripheral fiber activates cellular processes to greatly enhance neuroregeneration. Exploiting this “conditioned” form of regeneration for therapy requires a clear understanding of its underlying mechanisms, which is still lacking despite intense research in mammalian systems. Specifically, there is a knowledge gap regarding the impact of neuron type, morphology, and connectivity on regeneration. An in vivo approach in the worm C. elegans can reveal the cellular mechanisms underlying conditioned regeneration by femtosecond laser surgery and high-precision microscopy of single neuronal fibers. Three genes identified in the worm also modulate mammalian lesion conditioning, demonstrating that this approach can discover key conserved mechanisms. Even though this approach is effective at examining single genes or mechanisms, its manual execution precludes it from defining regenerative capacity across multiple neuron types and surgery locations. Thus, there is a critical need to accelerate imaging and laser surgery to comprehensively study regeneration. The overall objectives of the proposed project are to optimize an automated microscope platform and validate it by broadly testing many neuron types in C. elegans for conditioned regeneration. The rationale for this project is that an automated platform will permit large-scale regeneration studies that are currently impractical but required to fully map regenerative pathways. The objectives will be achieved by the following Specific Aims: 1) Improve image contrast to permit computer visualization of neurites. 2) Develop a real-time machine learning approach for automated neuron reconstruction. 3) Assess regenerative capacity in a broad range of neuron types in C. elegans. Work for Aim 1 will control the sample illumination and apply novel, real-time image processing to improve the contrast between neurons and their background. In Aim 2, these improved images will be reversibly compressed, computationally enhanced, reconstructed into a neuron model, and annotated for surgery. In Aim 3, the integrated platform will be used to perform surgery and reimage neurites in many neuron types in C. elegans to examine the role of key genes in regeneration. Innovative aspects of the proposed project include: an invertebrate model for lesion conditioning, new optical methods for improving imaging contrast, and novel machine learning techniques for real-time neuronal reconstruction. The expected outcomes of the proposed study are deep insights into the fundamental genetic and cellular mechanisms that determine the ability to execute conditioned regeneration and the validation of an automated microscope platform for high throughput imaging and surgery. These results are significant because they will establish important drivers of regeneration in the central nervous system, including potential therapeutic targets that could effectively treat currently incurable injuries and diseases of the nervous system.

项目总结/摘要 哺乳动物的中枢神经系统在受伤后通常无法再生，导致无法治愈的情况 巨大的医疗负担。一个例外是一个显着的效果称为损伤条件反射，其中损伤 神经元的外周纤维激活细胞过程，大大增强神经再生。利用这一 用于治疗的“条件”形式的再生需要对其潜在机制的清楚理解， 尽管在哺乳动物系统中进行了大量的研究，但仍然缺乏这种能力。具体来说，就是知识差距 关于神经元类型、形态和连接对再生的影响。一种体内方法， 沃姆角elegans可以通过飞秒激光揭示条件再生的细胞机制 单神经纤维的外科手术和高精度显微镜。在蠕虫中发现的三个基因也 调节哺乳动物损伤条件反射，表明这种方法可以发现关键的保守的 机制等尽管这种方法在检查单个基因或机制方面是有效的，但它的手册 执行排除了它定义跨多个神经元类型和手术位置的再生能力。 因此，迫切需要加速成像和激光手术以全面研究再生。 拟议项目的总体目标是优化自动化显微镜平台并对其进行验证 通过广泛测试C. elegans为条件再生。该项目的基本原理是 自动化平台将允许目前不切实际但需要的大规模再生研究 来全面绘制再生路径这些目标将通过以下具体目标来实现：1）提高 成像对比度以允许神经突的计算机可视化。2)开发实时机器学习方法 用于自动神经元重建。3)评估C中多种神经元类型的再生能力。 优美的Work for Aim 1将控制样品照明，并应用新颖的实时图像处理技术， 提高神经元和其背景之间的对比度。在目标2中，这些改进的图像将被可逆地 压缩、计算增强、重建成神经元模型并注释用于手术。在Aim中 3，该集成平台将用于在C中的许多神经元类型中进行手术和重新成像神经突。 elegans研究再生中关键基因的作用。拟议项目的创新方面包括： 用于病变调节的无脊椎动物模型，用于改善成像对比度的新光学方法，以及 用于实时神经元重建的机器学习技术。拟议工作组的预期成果 这项研究是对决定能力的基本遗传和细胞机制的深入了解， 执行条件再生和高通量自动化显微镜平台的验证 成像和手术。这些结果意义重大，因为它们将确立重要的再生驱动力 在中枢神经系统，包括潜在的治疗目标，可以有效地治疗目前 无法治愈的损伤和神经系统疾病。